A kinetic analysis of the tumor-associated galactopyranosyl-(1→3)-2-acetamido-2-deoxy-α-D-galactopyranoside antigen-lectin interaction

A kinetic study of the tumor-associated galactopyranosyl-(1→3)-2-acetamido-2-deoxy-α-D-galactopyranoside (T-antigen) with lectin peanut agglutinin is described. The disaccharide antigen was synthesized by chemical methods and was functionalized suitably for immobilization onto a carboxy-methylated sensor chip. The ligand immobilized surface was allowed interaction with the lectin peanut agglutinin, which acted as the analyte and the interaction was studied by the surface plasmon resonance method. The ligand-lectin interaction was characterized by the kinetic on-off rates and a bivalent analyte binding model was found to describe the observed kinetic constants. It was identified that the antigen-lectin interaction had a faster association rate constant (ka1) and a slower dissociation rate constant (kd1) in the initial binding step. The subsequent binding step showed much reduced kinetic rates. The antigen-lectin interaction was compared with the kinetic rates of the interaction of a galactopyranosyl-(1→4)-β -D-galactopyranoside derivative and a mannopyranoside derivative with the lectin

Synthesis, fluorescence and photoisomerization studies of azobenzene-functionalized poly(alkyl aryl ether) dendrimers

A series of azobenzene-functionalized poly(alkyl aryl ether) dendrimers have been synthesized and their photochemical and photophysical properties in solution and as thin films have been investigated. Although the photochemical behavior of the azodendrimers in solution indicated that the azobenzene units behave independently, very similar to the constituent monomer azobenzene unit, the properties of thin solid films of the dendrimers were distinctly different. The azodendrimers, AzoG1, AzoG2, and AzoG3 were observed to form stable supercooled glasses, which showed long-wavelength absorption and red emission characteristics of J-aggregates of the azobenzene chromophores. Reversible photoinduced isomerization of the azodendrimers in the glassy state is described

Synthetic (p)ppGpp analogue is an inhibitor of stringent response in mycobacteria

Bacteria elicit an adaptive response against hostile conditions such as starvation and other kinds of stresses. Their ability to survive such conditions depends, in part, on stringent response pathways. (p)ppGpp, considered to be the master regulator of the stringent response, is a novel target for inhibiting the survival of bacteria. In mycobacteria, the (p)ppGpp synthetase activity of bifunctional Rel is critical for stress response and persistence inside a host. Our aim was to design an inhibitor of (p)ppGpp synthesis, monitor its efficiency using enzyme kinetics, and assess its phenotypic effects in mycobacteria. As such, new sets of inhibitors targeting (p)ppGpp synthesis were synthesized and characterized by mass spectrometry and nuclear magnetic resonance spectroscopy. We observed significant inhibition of (p)ppGpp synthesis by Rel(Msm) in the presence of designed inhibitors in a dose-dependent manner, which we further confirmed by monitoring the enzyme kinetics. The Rel enzyme inhibitor binding kinetics were investigated by isothermal titration calorimetry. Subsequently, the effects of the compounds on long-term persistence, biofilm formation, and biofilm disruption were assayed in Mycobacterium smegmatis, where inhibition in each case was observed. In vivo, (p)ppGpp levels were found to be downregulated in M. smegmatis treated with the synthetic inhibitors. The compounds reported here also inhibited biofilm formation by the pathogen Mycobacterium tuberculosis. The compounds were tested for toxicity by using an MTT assay with H460 cells and a hemolysis assay with human red blood cells, for which they were found to be nontoxic. The permeability of compounds across the cell membrane of human lung epithelial cells was also confirmed by mass spectrometry

PAGER: A Framework for Failure Analysis of Deep Regression Models

Safe deployment of AI models requires proactive detection of potential prediction failures to prevent costly errors. While failure detection in classification problems has received significant attention, characterizing failure modes in regression tasks is more complicated and less explored. Existing approaches rely on epistemic uncertainties or feature inconsistency with the training distribution to characterize model risk. However, we show that uncertainties are necessary but insufficient to accurately characterize failure, owing to the various sources of error. In this paper, we propose PAGER (Principled Analysis of Generalization Errors in Regressors), a framework to systematically detect and characterize failures in deep regression models. Built upon the recently proposed idea of anchoring in deep models, PAGER unifies both epistemic uncertainties and novel, complementary non-conformity scores to organize samples into different risk regimes, thereby providing a comprehensive analysis of model errors. Additionally, we introduce novel metrics for evaluating failure detectors in regression tasks. We demonstrate the effectiveness of PAGER on synthetic and real-world benchmarks. Our results highlight the capability of PAGER to identify regions of accurate generalization and detect failure cases in out-of-distribution and out-of-support scenarios

Single Model Uncertainty Estimation via Stochastic Data Centering

We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a striking new finding that an ensemble of neural networks with the same weight initialization, trained on datasets that are shifted by a constant bias gives rise to slightly inconsistent trained models, where the differences in predictions are a strong indicator of epistemic uncertainties. Using the neural tangent kernel (NTK), we demonstrate that this phenomena occurs in part because the NTK is not shift-invariant. Since this is achieved via a trivial input transformation, we show that it can therefore be approximated using just a single neural network -- using a technique that we call $\Delta-$UQ -- that estimates uncertainty around prediction by marginalizing out the effect of the biases. We show that $\Delta-$UQ's uncertainty estimates are superior to many of the current methods on a variety of benchmarks -- outlier rejection, calibration under distribution shift, and sequential design optimization of black box functions

Hyperglycosylation of glycopeptidolipid of mycobacterium smegmatis under nutrient starvation: structural studies

The presence of a polar species of glycopeptidolipid (GPL) in carbon-starved Mycobacterium smegmatis has been reported previously. In this study, the complete structure of this GPL is established with the help of MALDI-TOF (matrix assisted laser desorption/ionization time of flight) and ESI (electrospray ionization) -MS, 13C-SEFT (spin echo Fourier transform) -NMR spectroscopy, and HPLC analysis. In the molecule, two units of a 3,4-di-O-methyl derivative of rhamnose are attached to L-alaninol via a 1 → 2 linkage. Various methyl derivatives of rhamnose and 6-deoxytalose were synthesized as standards to establish this structure. The accumulation of this polar GPL in M. smegmatis is sigB dependent, as a SigB-overproducing strain of M. smegmatis shows the presence of this spot in the exponential phase, and a sigB-knockout strain of M. smegmatis does not show the presence of any polar GPLs

Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection

Accurately detecting out-of-distribution (OOD) data with varying levels of semantic and covariate shifts with respect to the in-distribution (ID) data is critical for deployment of safe and reliable models. This is particularly the case when dealing with highly consequential applications (e.g. medical imaging, self-driving cars, etc). The goal is to design a detector that can accept meaningful variations of the ID data, while also rejecting examples from OOD regimes. In practice, this dual objective can be realized by enforcing consistency using an appropriate scoring function (e.g., energy) and calibrating the detector to reject a curated set of OOD data (referred to as outlier exposure or shortly OE). While OE methods are widely adopted, assembling representative OOD datasets is both costly and challenging due to the unpredictability of real-world scenarios, hence the recent trend of designing OE-free detectors. In this paper, we make a surprising finding that controlled generalization to ID variations and exposure to diverse (synthetic) outlier examples are essential to simultaneously improving semantic and modality shift detection. In contrast to existing methods, our approach samples inliers in the latent space, and constructs outlier examples via negative data augmentation. Through a rigorous empirical study on medical imaging benchmarks (MedMNIST, ISIC2019 and NCT), we demonstrate significant performance gains ($15\% - 35\%$ in AUROC) over existing OE-free, OOD detection approaches under both semantic and modality shifts